f young people to avoid naturalising a 'gateway' logic of connection that might ultimately inform the associative logic of young users themselves, and potentially the development of their usage careers.MicroRNAs (miRNAs) are promising biomarkers for the early diagnosis of breast cancer. Yet, simultaneous achievement of rapid, sensitive and accurate detection of diverse miRNAs in clinical samples is still challenging due to the low abundance of miRNAs and the complex procedures of RNA extraction and separation. Herein, we develop an innovative three-dimensional (3D) surface-enhanced Raman scattering (SERS) holography sensing strategy for rapid, sensitive and multiplexed detection of human breast cancer-associated miRNAs. To establish a proof of concept, nine kinds of human breast cancer-associated miRNAs are isothermally amplified by Exonuclease (Exo) III enzyme, and the products could be spatially separated to corresponding sensing region on silicon SERS substrates. Each region has been modified with corresponding hairpin DNA probes, which are used to identify and quantify the miRNAs. Different DNA probes are labeled with different Raman reporters, which serve as "SERS tags" to incorporate spectroscopic information into computer-generated 3D SERS hologram within ~9 min. We demonstrate that 3D SERS holography chip not only achieves an ultrahigh sensitivity down to ~1 aM but also feature a high correlation with RT-qPCR in the detection of nine miRNAs in 30 clinical serum samples. This work provides a feasible tool to improve the diagnosis of breast cancer.Artificial-intelligence population-based automated quantification of placental maturation and health from a rapid functional Magnetic Resonance scan. The placenta plays a crucial role for any successful human pregnancy. Deviations from the normal dynamic maturation throughout gestation are closely linked to major pregnancy complications. Antenatal assessment in-vivo using T2* relaxometry has shown great promise to inform management and possible interventions but clinical translation is hampered by time consuming manual segmentation and analysis techniques based on comparison against normative curves over gestation.
This study proposes a fully automatic pipeline to predict the biological age and health of the placenta based on a free-breathing rapid (sub-30 second) T2* scan in two steps Automatic segmentation using a U-Net and a Gaussian process regression model to characterize placental maturation and health. These are trained and evaluated on 108 3T MRI placental data sets, the evaluation included 20 higohort-level and for individual predictions. The proposed machine-learning pipeline runs in close to real-time and, deployed in clinical settings, has the potential to become a cornerstone of diagnosis and intervention of placental insufficiency. APPLAUSE generalizes to an independent cohort imaged at 1.5T, demonstrating robustness to different operational and clinical environments.
The presented automatic pipeline facilitates a fast, robust and reliable prediction of placental maturation. https://www.selleckchem.com/products/azd4573.html It yields human-interpretable and verifiable intermediate results and quantifies uncertainties on the cohort-level and for individual predictions. The proposed machine-learning pipeline runs in close to real-time and, deployed in clinical settings, has the potential to become a cornerstone of diagnosis and intervention of placental insufficiency. APPLAUSE generalizes to an independent cohort imaged at 1.5 T, demonstrating robustness to different operational and clinical environments.Tracking of particles in temporal fluorescence microscopy image sequences is of fundamental importance to quantify dynamic processes of intracellular structures as well as virus structures. We introduce a probabilistic deep learning approach for fluorescent particle tracking, which is based on a recurrent neural network that mimics classical Bayesian filtering. Compared to previous deep learning methods for particle tracking, our approach takes into account uncertainty, both aleatoric and epistemic uncertainty. Thus, information about the reliability of the computed trajectories is determined. Manual tuning of tracking parameters is not necessary and prior knowledge about the noise statistics is not required. Short and long-term temporal dependencies of individual object dynamics are exploited for state prediction, and assigned detections are used to update the predicted states. For correspondence finding, we introduce a neural network which computes assignment probabilities jointly across multiple detections as well as determines the probabilities of missing detections. Training requires only simulated data and therefore tedious manual annotation of ground truth is not needed. We performed a quantitative performance evaluation based on synthetic and real 2D as well as 3D fluorescence microscopy images. We used image data of the Particle Tracking Challenge as well as real time-lapse fluorescence microscopy images displaying virus structures and chromatin structures. It turned out that our approach yields state-of-the-art results or improves the tracking results compared to previous methods.Grasslands in Tohoku and North Kanto, Japan were contaminated with radiocesium released from the Fukushima Dai-ichi Nuclear Power Plant in March 2011. The dominant pasture species in the permanent grasslands of these areas is orchardgrass (Dactylis glomerata L.). Two field studies were conducted to determine the potential of a low radiocesium-uptake forage grass to replace orchardgrass for remediation of contaminated grasslands. From 2012 to 2014, tall fescue (Festuca arundinacea Schreb.) showed lower 137Cs uptake than orchardgrass under harvesting condition. The annual mean 137Cs activity concentration and transfer factor in tall fescue were half of those in orchardgrass. There was no significant difference in the 137Cs activity concentration among the five cultivars of tall fescue at the third harvest in 2012. From 2016 to 2019, another field study was conducted in a pasture with tall fescue and orchardgrass introduced by overseeding under heavy grazing pressure after the Fukushima Dai-ichi Nuclear Power Plant accident.